Project Summary/Abstract AD is a universally fatal disease that follows an average course of 10 years of progressive cognitive disability. Despite a dire need for successful treatment and prevention strategies, no disease-modifying therapies have been successfully brought to market. The Accelerating Medicines Partnership in Alzheimer's Disease Target Discovery project (AMP-AD) is working to identify candidate targets with potential therapeutic impact by querying AD-induced changes in molecular state on a systems level. Computational models of disease have emerged across six AMP-AD projects. Despite high-level consensus across models, initial efforts have identified largely non-overlapping sets of driver genes likely to regulate disease perturbation of network state. This project brings together leading PIs from several AMP-AD teams (Sage Bionetworks, Icahn Institute at Mount Sinai and the Institute for Systems Biology), investigators from the Structural Genomics Consortium/ Oxford Drug Discovery Institute and leading scientists from AMP-AD Industry partners with the goal to develop a set of tools that will address limitations in the existing AMP-AD program and support target validation for at least 10 AMP-AD nominated targets. This will be achieved through the following specific aims: 1. Develop the `Wall of Targets' as a visual dashboard and integrative analysis platform in support of cross-consortium AMP- AD target prioritization. 2. Develop a temporal model of disease progression to supplement existing target prioritization strategies with those focused on identification of early prevention targets. 3. Develop and disseminate an in silico high throughput drug screening tool to support network-based target prioritization and drug discovery efforts. 4. Develop tools and reagents to support experimental validation of at least 10 AMP-AD targets. The proposed work is designed to maximize the vast resources and biological insights within AMP-AD and to accelerate characterization and validation of AMP-AD targets through the development and open dissemination of a set of consortium-wide high-confidence therapeutic targets in conjunction with the computational and experimental tools necessary for their validation.